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Modern civilization is highly dependent on industrial agriculture. Industrial agriculture in turn has become an increasingly complex and globally interconnected system whose historically unprecedented productivity relies strongly on external energy inputs in the shape of machinery, mineral fertilizers, and pesticides. It leaves the system vulnerable to disruptions of industrial production and international trade. Several scenarios have the potential to damage electrical infrastructure on a global scale, including electromagnetic pulses caused by solar storms or the detonation of nuclear warheads in the upper atmosphere, as well as a globally coordinated cyber-attack. The current COVID-19 pandemic has highlighted the importance of crisis preparation and the establishment of more resilient systems. To improve preparation for high-stake risk scenarios their impact especially on critical supply systems must be better understood. To further the understanding of consequences for the global food system this work aims to estimate the effect the global inhibition of industrial production could have on the crop yields of maize, rice, soybean, and wheat. A generalized linear model with a gamma distribution was calibrated on current crop-specific gridded global yield datasets at five arcmin resolution. Gridded datasets on the temperature regime, the moisture regime, soil characteristics, nitrogen, phosphorus and pesticide application rates, the fraction of irrigated area and a proxy to determine whether farm activities are mechanized were chosen as explanatory variables. The model was then used to predict crop yields in two phases following a global catastrophe which inhibits the usage of any electric services. Phase 1 reflects conditions in the year immediately after the catastrophe, assuming the existence of fertilizer, pesticides, and fuel stocks. In phase 2 all stocks are used up and artificial fertilizer, pesticides and fuel are not available anymore. The predictions showed a reduction in yield of 10-30% in phase 1 and between 34 and 43% in phase 2. Overall Europe, North and South America and large parts of India, China and Indonesia are projected to face major yield reductions of up to 95% while most African countries are scarcely affected. The findings clearly indicate hotspot regions which align with the level of industrialization of agriculture. Further, it is shown that the yield reduction is likely to be substantial, especially in industrialized countries. The analysis also provides insights on major factors influencing crop yield under losing industry circumstances. Due to data unavailability some crucial factors could not be included in the model, but their qualitative discussion leads to the conclusion that the presented results can be considered an optimistic scenario, and that further research is needed to quantify the impact of the omitted aspects. 

How to cite: Jehn, F. U., Moersdorf, J., Rivers, M., Denkenberger, D., and Breuer, L.: Can we feed everyone without our modern infrastructure and industry? Simulating potential yield with a generalized linear model in a loss of industry scenario, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2619, https://doi.org/10.5194/egusphere-egu22-2619, 2022.

Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean, and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5 to -6% (SSP126) and +1 to -24% (SSP585) — explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9 shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

How to cite: Jägermeyr, J. and Müller, C. and the GGCMI Team: Climate change signal in global agriculture emerges earlier in new generation of climate and crop models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3011, https://doi.org/10.5194/egusphere-egu22-3011, 2022.

In rain-fed systems, efficient and timely crop planning is crucial to maximize crop production, adapt to climate variability, and increase the sustainability and resilience of the production systems. Smallholder farmers plan and anticipate possible interventions during the season based on the actual onset of the monsoon. However, their knowledge to define and predict the monsoon onset is limited to traditional methods whose predictive skill decreases significantly with a recent increase in both temperature and rainfall variability in the region. Therefore, defining the start of the monsoon accurately is a priority for improving crop production in rain-fed systems. Since the 1970s, researchers have produced more than 18 definitions—from local to regional scale—to define the start of the monsoon in the Sahel region which makes it difficult for one to find a suitable definition for a specific application. The present study compared and analyzed the West African Monsoon (WAM) onset according to Raman’s, Stern’s, Yamada’s, and Liebman’s definitions using station data from 13 locations in Senegal i.e. Dakar, Louga, Matam, St. Louis, Thies, Diourbel, Fatick, Kaffrine, Kaolack, Kedougou, Kolda, Tambacounda, and Ziguinchor from 1981 to 2020. To this end, we applied machine learning algorithms—K-means clustering and Decision Tree—to cluster the Sea Surface Temperature anomalies (SSTa) obtained from different regions of the Mediterranean and the Atlantic Ocean. We then used the clusters in the decision tree model to predict the onset and intensity of seasonal rainfall in the study locations according to the four definitions. Subsequently, we applied the set of the generated onset dates according to the four definitions as sowing dates in simulations of maize growth and yields using the  Agricultural Production Systems sIMulator (APSIM). Our analysis showed a statistically significant difference between the onset dates defined by the four definitions. Raman’s and Stern’s definitions delayed the monsoon onset at least two to four weeks after 1st June while Yamada’s and Liebman’s definitions delayed the onset one to two weeks after 1st June. Moreover, the amounts of seasonal rainfall in the season defined by Raman’s and Stern’s definitions were on average lower and more variable compared to those defined by Yamada’s and Liebman’s definitions. Similarly, we found statistically significant differences between the means of simulated maize yields in the four sets of sowing dates used. The highest yields with the lowest interannual variability were found in Yamada followed by Liebman’s sowing dates. The other sets of sowing dates had very low yields and higher variability compared to Yamada’s and Liebman’s sowing dates. We found the SSTa from the Southern Atlantic Ocean, Mediterranean Sea, and Tropical Atlantic Ocean regions as good predictors of both onset dates and intensity of the monsoon. The accuracy ranged from 50% to 80% depending on the location. 

How to cite: Joseph, J. E., Whitbread, A., and Roetter, R.: Assessment of the relations between crop yield variability and the onset and intensity of the West African monsoon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7338, https://doi.org/10.5194/egusphere-egu22-7338, 2022.

The long-term crop trials (LTE) provide valuable insights into functioning of the crop systems under variety of crop management strategies. In particular, those field operations which in long run affect the soil organic carbon balance might be of an importance for the climate change impacts oriented research. Bonded strongly to the local site conditions, LTEs provide spatially limited information, not fully reflecting the needs of the large-scale inventories covering countries or big regions. Representing LTEs with a process-based model via locally calibrated model parameters and data, and subsequent upscaling of the model with regional data on climate, terrain, soil, and land use, provides a possible way for LTEs extrapolation to wider geographical domains. As a follow-up to the earlier work on formalising LTE records from several sites in Czechia with the EPIC model, the simulation infrastructure (EPIC-IIASA (CZ)) has been created for regional predictions of crop production and its agro-environmental impacts over the whole territory of Czech Republic (CZ). Conceptually, the EPIC-IIASA (CZ) has been designed based on the EPIC-IIASA global gridded crop modelling system. A set of 977 spatial simulation units (or typical fields, > 1 ha each), which represent a unique combination of an administrative unit (level LAU1), climate region, and soil region, has been compiled using CZ national data. Each simulation unit has been used for linking spatially explicit input data on i) climate, ii) site, iii) soil properties, and iv) crop management to the process-based model EPIC. As an output, various agro-environmental variables may be acquired and visualized geographically. Initially, the spatial infrastructure worked with fixed sowing and harvesting dates across all CZ regions. In order to get the full potential of the EPIC-IIASA (CZ), a calibration with regional planting scenarios was done. Agronomically relevant planting-harvesting windows scenarios were assessed based on the published data (MOCA report), this specifically for traditional production areas in CZ (CZ_R01: Maize growing; CZ_R02: Potato growing; CZ_R03: Cereal growing; CZ_R04: Forage growing; CZ_R05: Sugar beet growing). Since there was not any yield data available for the LAU1 level administrative regions, published LAU1 estimates of the potential yields were used for validation of the EPIC-IIASA (CZ) simulated rainfed and nutrient-unlimited yields. Both absolute simulated yields and the percentage of reported potential yields were displayed geographically and spatial pattern of the simulated values evaluated. Furthermore, long-term average and inter-annual variability of simulated yields were compared to the available statistical data at the NUTS3 administrative level. To date, calibration and validation of two crops, spring barley and winter wheat were successfully performed. Other crops will be calibrated in the next step, so that representative crop rotations could be constructed and used in EPIC-IIASA (CZ) setup to properly approximate the prevailing regional cropping systems in the simulations. Such a completely calibrated and validated crop modelling system could serve as a powerful tool for extrapolating impacts of different crop management strategies, well explored with LTEs, over the larger areas, and hence, provide valuable evidence-based inputs for decision-making support at regional and national levels in CZ.

How to cite: Křížová, K., Skalský, R., Madaras, M., and Balkovič, J.: Extrapolation of the LTE data for regional prediction of crop production and agro-environmental impacts in the Czech Republic with the EPIC-based modelling system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7809, https://doi.org/10.5194/egusphere-egu22-7809, 2022.

Monitoring of crop growth, variability and dynamics over agricultural areas is needed to optimize management practices and thus to ensure global food security. Nonetheless, estimation of cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. 

Since 2017, the European Space Agency (ESA) Copernicus Sentinel-2A & B (S2) have been providing high resolution optical imagery all over the globe with an observation frequency of 5 days. With 13 spectral channels and 10-60m spatial resolution, time series of these data offer untapped potential for monitoring cultivated areas. In this respect, the processing of S2 imagery in cloud-based platforms, such as Google Earth Engine (GEE), allows large-scale precise mapping of agricultural fields. The arrival of GEE enabled us to propose an end-to-end processing chain for vegetation phenology characterization using S2 imagery at large scale.

To achieve this, the following pipeline was implemented: (1) building hybrid Gaussian process regression (GPR) models optimized with active learning (AL) for retrieval of crop traits, such as leaf area index (LAI), fractional vegetation cover (FVC), canopy chlorophyll content (laiCab), canopy dry matter content (laiCm) and canopy water content (laiCw), (2) implementing these models into GEE, (3) generating spatially continuous maps and gap-filled time series of these crop traits, and finally (4) calculating land surface phenology (LSP) metrics, such as start of season (SOS) or end of season (EOS), by using the conventional double logistic approach.

In respect to step (1): variable-specific training datasets were generated in the ARTMO software environment using PROSAIL model simulations, with training samples reduced in number but optimized in quality, i.e. representativeness, using the Euclidean-distance based (EBD) AL technique. In this way, light retrieval models were generated via GPR, a ML algorithm which builds up a retrieval model by learning the non-linear relationships between the spectral signals and crop traits of interest. Overall, good to high performance was achieved in particular for the estimation of canopy-level traits, such as LAI and laiCab, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. Subsequently, (2) the retrieval models were integrated into the GEE environment to perform mean value prediction on-the-fly. In this way, time series of crop traits based on S2 images were produced quasi-instantly over the area of interest. As demonstration of the workflow capability to easily reconstruct time series of S2 entire tiles, phenology maps from multiple crop traits were generated over an agricultural area in Castile and Leon, Spain. For this region also crop calendar data were available to assess the validity of the LSP metrics derived from crop traits. In addition, LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) were used as reference, demonstrating the good quality of the quantitative traits products to describe phenology. Thanks to the GEE framework, the proposed workflow can be carried out globally in any time window, thus representing a shift in satellite data processing towards cloud computing. 

How to cite: Salinero Delgado, M., Estévez, J., Pipia, L., Belda, S., Berger, K., Paredes Gómez, V., and Verrelst, J.: Quantifying agricultural traits and land surface phenology metrics in Google Earth Engine., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9944, https://doi.org/10.5194/egusphere-egu22-9944, 2022.

In the Bourgogne Franche-Comté region, climate change will lead to an increase of the field evapotranspiration during the crop cycle and a modification of the rainfall distribution within the year, leading to longer and more intense drought periods in summer. This will increase the crop water requirement, while reducing water availability and accessibility, which could negatively impact agricultural productivity and stability. It is thus necessary to evaluate actual and future water deficiency in a way to develop and implement adapted responses (new farming practices, new crops, water storage…). Based on simulated weather data (rainfall and potential evapotranspiration) integrating the effect of climate change until 2100, soil characteristics (texture and depth) and crop water requirement, we estimate the daily water balance at the scale of the Bourgogne Franche-Comté region, France. We use weather data at an 8*8 square-kilometres grid and soil water capacity is estimated at the soil map unit using the methodology developed by Bruand et al. (2004). This methodology estimates the water capacity of each layer of the soil unit based on their texture class (Aisne triangle) and removing the proportion of gravels and rocks considering that their contribution to water storage is negligible. For the 10 main crops of the region in terms of field area (i.e. grassland, winter wheat, winter barley, winter oilseed rape, spring barley, maize, soybean, sunflower, winter peas and spring peas), we calculate the water balance using the methodology developed by Jacquart and Choisnel (1995) at the scale of the intersection between the weather grid and the soil map unit in a way to represent homogeneous pedoclimatic territories. The soil water capacity is thus divided in two reservoirs with no horizontal transfer. Water from the first reservoir (40% of the soil water capacity) is easily accessible to the crop while water from the second reservoir is less and less accessible as the reservoir is emptied. At a daily step, the meeting of the crop water requirement is estimated regarding the water available in the soil reservoirs and the rainfalls. This study enables to estimate the actual water deficiency of the main crops and its potential increase due to climate change. We can thus identify crops that could not be cropped anymore without irrigation in some area and estimate the water required if we want to keep these crops in the future. These results are also important to evaluate if it is possible to developed new practices or water storage in response to the effects of climate change. Our approach allows as well to evaluate and anticipate the possibility to implement new crops requiring less water, avoiding the drought periods or able to access more water in the soil. These results will allow the agricultural sector to develop outlets for these new crops.

How to cite: Yvoz, S., Lechenet, M., Amiotte-Suchet, P., and Ubertosi, M.: Impact of the climate change on the crop water deficiency at the regional scale: study case in Bourgogne Franche-Comté, France., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7514, https://doi.org/10.5194/egusphere-egu22-7514, 2022.

The water footprint (WF) of crop production indicates the water consumption for crop growth in a specific area over a certain time, enabling comprehensive water use efficiency assessments to be achieved for different types of water. Improved spatial and temporal resolutions in quantification enable the water footprint (WF) in crop production to be a comprehensive indicator of water consumption in agricultural water management. However, in time, although daily and monthly blue (irrigation water) and green (rainfall) water resources are unevenly distributed in monsoon climate areas, the crop WF are generally recorded by years. In space, there is a lack of quantitative research on the effects of different spatial levels on the variation in crop WFs. Meanwhile, effect of developments in water-saving irrigation techniques on large-scale crop WF accounting is unclear yet.

We conducted a series of case studies for China in order to address above three issues. In the first selected case for maize and wheat production in the Baojixia Irrigation District (BID) of Shaanxi province in the west China, the WF of crop production was analysed based on a regional distributed hydrological model and the associated meteorological driving factors on daily, monthly, and yearly scales were identified (Gao et al., 2021). The latter two case studies focused on wheat across the whole mainland China based on gridded crop WF simulations over the period 2000-2014. The WFs of wheat production at five different spatial levels, including crop field, county, river sub-basin, provincial, and large river basin were mapped followed by an analysis of meteorological and human management factors (Mao et al., 2021). The differences in terms of magnitudes, composition, and benchmarks of wheat WF under furrow, sprinkler and micro irrigation methods as well as rain-fed conditions were further distinguished and identified (Wang et al., 2019). Results revealed non-negligible effects of temporal and spatial scales on crop WFs. The possibility and importance to account for developments of water-saving techniques in regional crop WF estimations are shown as well.

References

Gao, J., Xie, P., Zhuo, L., Shang, K., Ji, X., Wu, P. (2021) Water footprints of irrigated crop production and meteorological driving factors at multiple temporal scales. Agricultural Water Management, 255: 107014.

Mao, Y., Liu, Y., Zhuo, L., Wang, W., Li, M., Feng, B. (2021) Quantitative evaluation of spatial scale effects on regional water footprint in crop production. Resources, Conservation & Recycling, 173: 105709.

Wang, W., Zhuo, L., Li, M., Liu, Y., Wu, P. (2019) The effect of development in water-saving irrigation techniques on spatial-temporal variations in crop water footprint and benchmarking. Journal of Hydrology, 577: 123916.

How to cite: Zhuo, L., Gao, J., Wang, W., Liu, Y., and Wu, P.: Drivers of water footprints in crop production across different temporal and spatial scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4860, https://doi.org/10.5194/egusphere-egu22-4860, 2022.

Soil pH is one of the most important properties for soil health, affecting the microbial activities, aggregate structure, nutrient availability and soil toxicity. For croplands in China, intensive application of ammonium-based fertilizer, as well as increased rate of nitrogen deposition, are inducing significant soil acidification in the long term and therefore threating agricultural sustainability. However, in almost all process-based biogeochemical models, soil pH is used as model input (constant) but its dynamic (especially at decadal scale) has not been simulated properly. In this study, we developed the new soil pH module in GDNDC (Gridded version of DeNitrification and DeComposition model) model to simulate the evolution of soil acidification processes within 0-40cm depth and its effect on crop growth. Using charge balance as the principle, different equations based on the chemical equilibrium between H⁺, NH₄⁺, NO₃^-, Al³⁺, base cations (e.g. Mg²⁺, Ca²⁺ and K⁺), organic anion (Org^-) and CO₂ were integrated into the new model and then numerically solved at daily step. Over 20-year field observations (e.g. soil nitrogen content, soil pH, crop yield, etc) under different fertilization scenarios (including non-fertilizer, inorganic NPK only, organic manure only, and inorganic NPK + organic manure) from both Qiyang and Jinxian sites in China were used to validate the accuracy of model’s prediction. By comparing the model outputs with field measurements, we found the GDNDC (v2.0) could effectively capture the unique trend of soil pH evolution under different fertilization scenarios at decadal scale, for example, the accelerated soil acidification under NPK and the buffering effect of organic manure. The difference of crops yield under different fertilization scenarios was also predicted precisely. As a result, our model has the capacity to simulate the dynamic of soil pH under various fertilization schemes and it can make a great contribution to long-term policy making on improved fertilization for agricultural sustainability.

How to cite: Huang, X. and Yu, C.: GDNDC (v2.0): Modelling long-term soil acidification of cropland under different fertilization scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1066, https://doi.org/10.5194/egusphere-egu22-1066, 2022.

Sustainable agriculture is based on the responsible use of soil resources. Soil organic matter (SOM) is one of the most important properties and would be taken in consideration in any modelization associated to the mitigation of climate change. The estimation of SOM has been widely obtained based on two main methodologies: ignition and oxidation. The method to measure soil organic matter by loss on ignition (LOI) is considered an easy and fast method. However, some interactions depending on the temperature used and the presence of carbonates, can produce overestimations. On the contrary, the Walkley-Black method (WB) is a relatively accurate method based on the oxidation of organic matter but recalcitrant carbon substances can resist this oxidative attack.

Our preliminary study aims to evaluate the relationships between these two methods in calcareous agricultural soil samples taken in the province of Alicante, in the South East of Spain. Land use were divided in three main agricultural uses: horticultural crops, fruit crops and pasture. For this purpose, 41 sites were sampling, 16 samples belong to horticultural systems, 8 to fruit crops and 17 in pasture. The samples were collected at a depth of (0-20cm).

The results of the organic matter content (mean value and standard deviation) expressed in g/kg for LOI (4h. at 380^oC) and WB (traditional method) were the following for each land use: horticultural LOI=114±24 and WB=31±7; fruit crops LOI=97±4 and WB=63±51; and pasture LOI=66±19 and WB=32±17. After that, a simple linear regression was used to compare LOI and WB. The results showed the following: R²=0.31 and p < 0.01; R²=0.74 and p < 0.05; and R²=0.41 and p < 0.001; for horticultural, fruits crops and pasture land use respectively. The relation between both methods was higher under fruit crops. The mean value of carbonates for each land use group were: 112±6 in horticultural soils; 113±6 in fruits cropping system; and 11±4 in pasture. A simple linear regression was used again to compare LOI-Carbonates and WB-Carbonates in horticultural systems, fruit crops and pasture land use. In this case, the Pearson correlations were R²=0.62, p < 0.01 and R²=0.16, p < 0.001; R²=0.08, p < 0.01 and R²=0.6, p < 0.001; R²=0.006, p < 0.001 and R²=0.08, p < 0.01; respectively. No linear dependence between two variables analysed (LOI-Carbonates and WB-Carbonates) was found in any farming system.

The relation between soil organic matter content determined by using LOI and WB, revealed that a good relation was found in the pasture land use, which reflects that in uncultivated soils, organic matter would tend to the stabilization. On the other hand, the relation between soil organic matter content and carbonates, indicates that there is no relationship between them, excepting for the relationship between LOI and carbonates in horticultural soils, which may indicate that carbonates are easily degradable in cultivated soils (under intensive agriculture) and their presence can overestimate or has some influence on the soil organic matter content obtained by using the LOI method. However, more research is need to obtain satisfactory results.

How to cite: Navarro Pedreño, J., Benslama, A., Gómez Lucas, I., and Almendro Candel, M. B.: Organic matter in farming systems in Southern Spain by LOI and Walkley-Black methods., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9368, https://doi.org/10.5194/egusphere-egu22-9368, 2022.

The amount of data that can be being collected and openly distributed is increasing daily, but only a fraction of this data is being used to constrain models of agricultural systems under global change and help land managers make informed land-use decisions. Until we can make models accessible to the greater public and bring models and data together, none of these resources can be used to their full potential.

Here we present the (RETINA) project: Monitoring, Reporting and Verification (MRV) systems implemented at the farm level, used to quantify soil carbon change and greenhouse gasses (GHG) emissions combined with novel approaches in predictive modelling and stakeholder engagement, culminating in negative emission strategies in managed ecosystems.

By developing a dynamic digital system that connects multi-scale sensors using AI to novel cloud-based soil carbon and GHG modelling approaches, we can detect changes in organic matter and GHG emissions across land uses. Additionally, individual user-based inputs through a mobile RETINA app capture changes in agriculture management. This data is used within the Predictive Ecosystem Analyser framework to produce forecasts of GHG emissions and carbon sequestration. Landowners are provided with decision tools to not only interpret the effects of current land management practices on future emissions and carbon sequestration, but also to explore alternative interventions that can help mitigate the effects of climate change. This study to demonstrates smart farming at a local scale, however, these approaches are applicable globally. The RETINA project provides an accessible, automated and repeatable framework that moves us towards realizing this goal.

How to cite: Cowdery, E. and Yeluripati, J.: The RETINA Project: Dynamic monitoring, reporting and verification for implementing negative emission strategies in managed ecosystems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12764, https://doi.org/10.5194/egusphere-egu22-12764, 2022.

In the last decades, Mediterranean agricultural systems have experienced significant changes in land use and agricultural practices under the pressure of the worldwide market competition, the effects of the global changes and the need to contain the environmental impacts (Bajocco et al. 2012). The Mediterranean is characterized by peculiar traits (climate, soil, orography, traditions, etc.) which are rooted in history and allow us to distinguish this agriculture from those developed in the different European regions (Debolini et al., 2018).

The study aims to describe and interpret the expansion and specialization of agricultural systems in the Western Mediterranean areas in terms of land use (cereals, forage, vegetables, etc.) by using the data from the agricultural censuses of France (FR), Italy (IT), Portugal (PT) and Spain (ES) over the 2000-2010 period. In this study, first, we chose to limit the analysis to four European countries in order to improve the accuracy and the homogeneity of the data to process. Secondly, we matched each record of data-base (single municipality) to its geographical position (Land Unit = LU) to make possible the selection of the portions of territory that can be classified as Mediterranean, according to the EU classification (Sundseth, 2009).Third, we had to verify the agreement of the different categories of crop grouping used in the different national agricultural censuses and to integrate any missing information.

For the present study, the variables selected were: TAA (Total agricultural area), UAA (Utilized agricultural area), AL (Arable lands), PWC (Permanent Woody Crops), PFC (Permanent fodder crops), and RS (Remaining Surface) totaling a dataset with approximately 16,000 records. In addition, all records of the database were georeferenced with GIS to enable the geographical evaluation of the spatial data distribution. The LAU data analysis was carried out following the four steps: (1) level of land occupation by agricultural systems; (2) patterns of crop groups in UAA composition; (3) attribution to each LAU of an agricultural typology (AT), resulting from the combination of the two previous features; (4) calculation two indexes: Expansion Index (EXP) and the Specialization Index (SPE).

Results showed lowering overtime of the TFA, UAA, and PG areas and an increase of IA and RS. The number of identified ATs was rising at the expense of their extension. This phenomenon led to a fragmentation in ATs spatial distribution within the same geographical region. Even if the range of time was short for a global analysis, we identified different interesting trends of agricultural systems, which could be confirmed with the next census expected in 2022. These aspects will be useful to make a correct diagnosis about the current Mediterranean agroecosystems and to verify if they can preserve agricultural productivity and increase the resilience of rural societies.

How to cite: Rabelo, M., Debolini, M., Sabbatini, T., Villani, R., and Silvestri, N.: Global Land-Use Analysis in the Western Mediterranean area by integrating information from European Agri-Census data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12983, https://doi.org/10.5194/egusphere-egu22-12983, 2022.